pdf-comments-extractor 0.1.0

Extract comments, annotations, and structured content from PDF documents

PDF Comments Extractor

A powerful Python package for extracting comments, annotations, and structured content from PDF documents. Perfect for analyzing supervisor feedback, extracting academic paper content, and processing annotated PDFs.

Features

• 📝 Extract PDF Comments: Pull all annotations (highlights, sticky notes, text comments) from PDFs
• 📄 Academic Paper Extraction: Extract structured content from research papers (sections, citations, references, figures, tables)
• 🔍 Text Search: Search for specific text across PDFs with context
• 💾 Multiple Output Formats: Export to JSON, CSV, or both
• 🖥️ CLI Tools: Easy-to-use command-line interfaces
• 🐍 Python API: Full programmatic access for integration into your workflows
• 🌍 Multi-language Support: Works with any language including Hebrew, Arabic, Chinese, etc.

Installation

From PyPI (recommended)

pip install pdf-comments-extractor

From Source

git clone https://github.com/yourusername/pdf-comments-extractor.git
cd pdf-comments-extractor
pip install -e .

Quick Start

Command Line

Extract Comments from PDF

# Extract all comments
pdf-comments-extract paper.pdf

# With console output
pdf-comments-extract paper.pdf --print

# Specify output directory
pdf-comments-extract paper.pdf --output-dir ./my_comments

# Multiple files
pdf-comments-extract file1.pdf file2.pdf file3.pdf

Extract Academic Paper Content

# Extract all structured content
pdf-academic-extract paper.pdf --print

# JSON output only
pdf-academic-extract paper.pdf --format json

Search for Text in PDF

pdf-search paper.pdf "your search text"

Python API

Extract Comments

from pdf_comments_extractor import PDFCommentExtractor

# Using context manager (recommended)
with PDFCommentExtractor('paper.pdf') as extractor:
    comments = extractor.extract_all_comments()
    
    # Save to JSON
    extractor.save_to_json(comments, 'comments.json')
    
    # Save to CSV
    extractor.save_to_csv(comments, 'comments.csv')
    
    # Print summary
    extractor.print_summary(comments)

# Access individual comments
for comment in comments:
    print(f"Page {comment['page']}: {comment['comment']}")
    print(f"References: {comment['referenced_text']}")

Extract Academic Paper Content

from pdf_comments_extractor import AcademicPaperExtractor

extractor = AcademicPaperExtractor('paper.pdf')
data = extractor.extract_all()

# Access different components
print(f"Abstract: {data['sections']['Abstract']}")
print(f"Total citations: {data['statistics']['total_citations']}")

# Get all citations
for citation in data['citations']:
    print(f"{citation['author']} [{citation['year']}] on page {citation['page']}")

# Save results
extractor.save_to_json(data, 'paper_analysis.json')
extractor.close()

Search for Text

from pdf_comments_extractor import search_text_in_pdf

results = search_text_in_pdf('paper.pdf', 'neural networks')

for page_num, context in results:
    print(f"Found on page {page_num}:")
    print(context)

Output Format

Comment Extraction

Comments are extracted with the following information:

{
  "page": 1,
  "type": "Highlight",
  "subject": "Note",
  "author": "Reviewer Name",
  "comment": "This needs clarification",
  "referenced_text": "The text that was highlighted...",
  "position": {
    "x0": 100.5,
    "y0": 200.3,
    "x1": 300.2,
    "y1": 220.8
  },
  "created": "D:20231120120000",
  "modified": "D:20231120130000",
  "color": {"stroke": [1.0, 0.0, 0.0], "fill": null}
}

Academic Paper Extraction

Extracts comprehensive academic paper structure:

• Metadata: Title, authors, page count, word count
• Sections: Abstract, Introduction, Methodology, Results, Discussion, Conclusion
• Citations: All citations with context and page numbers
• References: Full bibliography
• Figures & Tables: Captions and locations
• Key Contributions: Identified contribution statements
• Statistics: Complete metrics

Use Cases

1. Supervisor Feedback Analysis

# Extract all supervisor comments
with PDFCommentExtractor('thesis_reviewed.pdf') as extractor:
    comments = extractor.extract_all_comments()
    
# Filter by author
supervisor_comments = [c for c in comments if c['author'] == 'Dr. Smith']

# Group by page
from collections import defaultdict
by_page = defaultdict(list)
for comment in comments:
    by_page[comment['page']].append(comment)

2. Literature Review

# Extract citations from multiple papers
from pathlib import Path

all_citations = []
for pdf in Path('papers/').glob('*.pdf'):
    extractor = AcademicPaperExtractor(str(pdf))
    data = extractor.extract_all()
    all_citations.extend(data['citations'])
    extractor.close()

# Analyze citation patterns
from collections import Counter
authors = [c['author'] for c in all_citations]
most_cited = Counter(authors).most_common(10)

3. Multi-language Support

# Works with Hebrew, Arabic, Chinese, etc.
with PDFCommentExtractor('hebrew_manuscript.pdf') as extractor:
    comments = extractor.extract_all_comments()
    
for comment in comments:
    if 'לא מובן' in comment['comment']:  # "not clear" in Hebrew
        print(f"Clarity issue on page {comment['page']}")

Requirements

• Python 3.7+
• PyMuPDF (automatically installed)

Development

Setup Development Environment

git clone https://github.com/yourusername/pdf-comments-extractor.git
cd pdf-comments-extractor
pip install -e ".[dev]"

Run Tests

pytest tests/

Format Code

black pdf_comments_extractor/

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this package in your research, please cite:

@software{pdf_comments_extractor,
  author = {Goldberg, Alexander},
  title = {PDF Comments Extractor},
  year = {2024},
  url = {https://github.com/yourusername/pdf-comments-extractor}
}

Acknowledgments

• Built with PyMuPDF
• Inspired by the need to efficiently process supervisor feedback on academic papers

Changelog

Version 0.1.0 (2024-11-29)

• Initial release
• PDF comment extraction
• Academic paper content extraction
• Search functionality
• CLI tools
• JSON and CSV export

Support

If you encounter any issues or have questions:

• Open an issue
• Check the documentation

Related Projects

• PyMuPDF - The underlying PDF library
• pdfplumber - Alternative PDF extraction tool
• PyPDF2 - Another PDF library

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Made with ❤️ by Alexander Goldberg